Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Nagiliant/Genesis-Legacy-V2 --skill genesis-a5git clone --depth 1 https://github.com/Nagiliant/Genesis-Legacy-V2Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nagiliant/genesis-legacy-v2/genesis-a5)<a href="https://agentmods.dev/skills/nagiliant/genesis-legacy-v2/genesis-a5"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a5/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nagiliant/genesis-legacy-v2/genesis-a5"><img src="https://agentmods.dev/badge/skills/nagiliant/genesis-legacy-v2/genesis-a5.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00903 |
| Opus 5 | $0.00023 | $0.00451 |
| Sonnet 5 | $0.00009 | $0.00181 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
A5 — Web Audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A5 — Web Audit (Research the World)
The outside-knowledge check — the only lens that brings information from beyond the project. A5 performs real web research (live searches, fetching real URLs, citing real sources) to find what the best teams in the world do for every significant technology, pattern, and domain in the implementation, then evaluates the build against world-class practice. Superior approaches it discovers are implemented immediately, not deferred.
Inputs
- The implementation code in scope (to extract research topics) · the micro specs · the constitution (the tech stack)
- Prior A1–A4 reports · the live web (search + fetch) · the project mode
- On a re-run: the prior A5 report and prior
research-learnings.md
Process
- Identify search topics — analyze the code/templates and extract technologies, patterns, problem domains, and specific challenges; each becomes a research thread.
- Search the web per topic, targeting authoritative sources: official docs and engineering blogs, conference talks and papers, RFCs and standards bodies, industry-leading implementations, UX/design-pattern libraries. (Content/process mode researches how professionals in the domain do their work.)
- Evaluate each finding: relevant to what we built (confidence 1–10)? credible source? better than our current approach? implementable now or blocked?
- Categorize: Superior approach → implement NOW · General learning → log it · Confirms our approach → record as validation · Not applicable → discard with a reason.
- Implement superior approaches immediately — fixed on the branch, like A2/A3 fixes. If too large to do now, create a new tracked micro spec — never merely note it.
- Persist general learnings to
research-learnings.md, tagged with technology, domain, and source URL — they carry across features. - Degrade gracefully: if the web is unavailable, log a skip and do not block the pipeline (run it when connectivity returns).
- Write the report to
audits/{scope}-a5.md: research areas searched (and why), per-finding source URL + relevance + category + action taken + confidence, the improvements implemented, the learnings recorded, validations, reasoning traces, and a competitive-position analysis.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 52 lines · 46 tokens per session scan A 73648d57d986
A5 — Web Audit is a skill published in the GitHub repository Nagiliant/Genesis-Legacy-V2 (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 903 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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